Model for Generating Scale-Free Artificial Social Networks Using Small-World Networks

作     者：Farhan Amin Gyu Sang Choi 

作者机构：Department of Information and Communication EngineeringYeungnam UniversityGyeongsan38541Korea 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第73卷第12期

页      面：6367-6391页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：This work was supported in part by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education under Grant NRF-2019R1A2C1006159 and Grant NRF-2021R1A6A1A03039493 in part by the 2021 Yeungnam University Research Grant 

主　　题：Social networks small-world networks network generation models graph theory random walk network design social network analysis 

摘      要：The Internet of Things(IoT)has the potential to be applied to social networks due to innovative characteristics and sophisticated solutions that challenge traditional *** network analysis(SNA)is a good example that has recently gained a lot of scientific *** has its roots in social and economic research,as well as the evaluation of network science,such as graph *** in this area have subverted predefined theories,offering revolutionary ones regarding interconnected networks,and they have highlighted the mystery of six degrees of separation with confirmation of the small-world *** motivation of this study is to understand and capture the clustering properties of large networks and social *** present a network growth model in this paper and build a scale-free artificial social network with controllable clustering *** random walk technique is paired with a triangle generating scheme in our proposed *** a result,the clustering controlmechanism and preferential attachment(PA)have been *** research builds on the present random walk *** took numerous measurements for validation,including degree behavior and the measure of clustering decay in terms of node degree,among other ***,we conclude that our suggested random walk model is more efficient and accurate than previous state-of-the-art methods,and hence it could be a viable alternative for societal evolution.